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Artificial intelligence is a rapidly evolving field that has experienced exponential growth over the past decade. During that time, the accumulation of vast amounts of data has propelled large and small corporations into the AI revolution.

At my company Standigm, which specializes in workflow AI drug discovery, AI is the primary tool for meeting our objective of lowering the time and cost required for designing and producing hit compounds, lead compounds and preclinical candidates. But we have serious competition: Big Tech.

For those unfamiliar with the concept, AI-based drug discovery primarily serves two purposes: fostering creativity and enhancing workflow efficiency. Because patents—demanding novelty and progression—are the primary assets of pharmaceutical companies, there’s a continuous demand for innovative molecules. AI leverages vast datasets to suggest improved molecules or previously undiscovered ones for targeted diseases by creating an entirely new workflow rooted in advanced decision-making processes.

In this way, AI could potentially revolutionize the entire structure of pharmaceutical R&D projects. Consequently, Big Tech companies have entered the AI drug discovery arena. For instance, Google has disrupted the protein structure prediction field with AlphaFold2, achieving superior results with sophisticated AI-based algorithms. Likewise, Microsoft has been investing heavily in basic science fields, including drug discovery.

Big Tech companies’ presence in the field is hardly surprising, as they consistently explore areas where their technologies can be applied. In an era ruled by AI technology, it is just a matter of time before a handful of successful companies disrupt the entire industry with groundbreaking ideas. However, superior technology does not necessarily guarantee business success—or in the case of biopharma, better medicines with less time and financial investment.

When multiple types of technologies integrate, it is essential to bridge the gaps emerging from differences in education, culture, philosophy and evaluation logic. While biopharma companies may not fully grasp AI’s disruptive potential, Big Tech companies might fail to understand the intricacies of drug development, such as the complex and rigorous government regulations that must be navigated to bring new treatments to market. For Big Tech to succeed, it’s crucial to appreciate these knowledge gaps and devise strategies to bridge them. If they can do that, Big Tech companies could disrupt the industry by leveraging their data aggregation, management and analysis strengths.

However, the influence of small- to mid-size companies focusing solely on AI drug discovery—companies such as Standigm—must not be underestimated. Large players with legacy systems often make misguided decisions based on previous successes, even if the situation calls for a different solution. In contrast, new players exhibit maximum flexibility. While it’s uncertain which company will achieve maximum success, I believe genuine innovation will stem from smaller companies rather than larger ones.

Drug discovery and development necessitate contextually rich data and experts trained to generate, manage and analyze them. Considering this complexity, AI-driven drug discovery and development is still in its infancy, with Insilico’s recently announced Phase II clinical trial of an AI-created drug being the furthest milestone achieved in the industry. Despite the challenges that lie ahead, I envision innovation stemming from the amalgamation of AI technology with the automation of experiments across various drug discovery and development areas in the near future.

Hanjo Kim is the senior VP of Global Strategy and head of Medicinal Chemistry at Standigm, a workflow AI-driven drug discovery company headquartered in Seoul, South Korea. Reach him on LinkedIn.